






"""simple docstring"""






import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



def    _A     (__a				)					->			List[str]:
    """simple docstring"""






    return 1.0 / (1.0 + np.exp(-_outputs				))



def    _A     (__a				)					->			List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						np.max(_outputs       ,							axis=-1       ,							keepdims=_lowerCamelCase				)
    SCREAMING_SNAKE_CASE_ :    List[Any]       		=						np.exp(_outputs - maxes				)
    return shifted_exp / shifted_exp.sum(axis=-1       ,							keepdims=_lowerCamelCase				)




class 				lowerCAmelCase__							(     lowercase__					):






      '''simple docstring'''

      __UpperCamelCase      					=    "sigmoid"
      __UpperCamelCase      					=    "softmax"
      __UpperCamelCase      					=    "none"




@add_end_docstrings(
    lowercase__							,						r"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "							,						)
class 				lowerCAmelCase__							(     lowercase__					):






      '''simple docstring'''

      __UpperCamelCase      					=    False
      __UpperCamelCase      					=    ClassificationFunction.NONE
      def __init__(		self	:    Dict		,      **lowercase_	:    Optional[int]):




          '''simple docstring'''


          super().__init__(**SCREAMING_SNAKE_CASE_)

          self.check_model_type(
              TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
              if self.framework == '''tf'''
              else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)
      def _SCREAMING_SNAKE_CASE					(		self	:    int		,      lowercase_	:    List[str]=None		,      lowercase_	:    List[Any]=None		,      lowercase_	:    List[str]=""		,      **lowercase_	:    Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    Dict       		=						tokenizer_kwargs

          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						{}
          if hasattr(self.model.config		,      '''return_all_scores''') and return_all_scores is None:
              SCREAMING_SNAKE_CASE_ :    List[Any]       		=						self.model.config.return_all_scores

          if isinstance(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_) or top_k is None:
              SCREAMING_SNAKE_CASE_ :    int       		=						top_k
              SCREAMING_SNAKE_CASE_ :    Dict       		=						False
          elif return_all_scores is not None:
              warnings.warn(
                  '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
                  ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''		,      SCREAMING_SNAKE_CASE_		,      )
              if return_all_scores:
                  SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						None
              else:
                  SCREAMING_SNAKE_CASE_ :    Dict       		=						1

          if isinstance(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_):
              SCREAMING_SNAKE_CASE_ :    List[Any]       		=						ClassificationFunction[function_to_apply.upper()]

          if function_to_apply is not None:
              SCREAMING_SNAKE_CASE_ :    List[Any]       		=						function_to_apply
          return preprocess_params, {}, postprocess_params
      def __call__(		self	:    Tuple		,      *lowercase_	:    str		,      **lowercase_	:    Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    Any       		=						super().__call__(*SCREAMING_SNAKE_CASE_		,      **SCREAMING_SNAKE_CASE_)
          # TODO try and retrieve it in a nicer way from _sanitize_parameters.
          SCREAMING_SNAKE_CASE_ :    Tuple       		=						'''top_k''' not in kwargs
          if isinstance(args[0]		,      SCREAMING_SNAKE_CASE_) and _legacy:
              # This pipeline is odd, and return a list when single item is run
              return [result]
          else:
              return result
      def _SCREAMING_SNAKE_CASE					(		self	:    Optional[Any]		,      lowercase_	:    int		,      **lowercase_	:    str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						self.framework
          if isinstance(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_):
              return self.tokenizer(**SCREAMING_SNAKE_CASE_		,      return_tensors=SCREAMING_SNAKE_CASE_		,      **SCREAMING_SNAKE_CASE_)
          elif isinstance(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_) and len(SCREAMING_SNAKE_CASE_) == 1 and isinstance(inputs[0]		,      SCREAMING_SNAKE_CASE_) and len(inputs[0]) == 2:
              # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
              return self.tokenizer(
                  text=inputs[0][0]		,      text_pair=inputs[0][1]		,      return_tensors=SCREAMING_SNAKE_CASE_		,      **SCREAMING_SNAKE_CASE_)
          elif isinstance(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_):
              # This is likely an invalid usage of the pipeline attempting to pass text pairs.
              raise ValueError(
                  '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
                  ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.''')
          return self.tokenizer(SCREAMING_SNAKE_CASE_		,      return_tensors=SCREAMING_SNAKE_CASE_		,      **SCREAMING_SNAKE_CASE_)
      def _SCREAMING_SNAKE_CASE					(		self	:    Tuple		,      lowercase_	:    Optional[Any]):




          '''simple docstring'''


          return self.model(**SCREAMING_SNAKE_CASE_)



      def _SCREAMING_SNAKE_CASE					(		self	:    Union[str, Any]		,      lowercase_	:    Optional[Any]		,      lowercase_	:    Any=None		,      lowercase_	:    List[Any]=1		,      lowercase_	:    Optional[int]=True):




          '''simple docstring'''


          if function_to_apply is None:
              if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                  SCREAMING_SNAKE_CASE_ :    Dict       		=						ClassificationFunction.SIGMOID
              elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                  SCREAMING_SNAKE_CASE_ :    List[Any]       		=						ClassificationFunction.SOFTMAX
              elif hasattr(self.model.config		,      '''function_to_apply''') and function_to_apply is None:
                  SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						self.model.config.function_to_apply
              else:
                  SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						ClassificationFunction.NONE

          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						model_outputs['''logits'''][0]
          SCREAMING_SNAKE_CASE_ :    List[Any]       		=						outputs.numpy()

          if function_to_apply == ClassificationFunction.SIGMOID:
              SCREAMING_SNAKE_CASE_ :    List[Any]       		=						sigmoid(SCREAMING_SNAKE_CASE_)
          elif function_to_apply == ClassificationFunction.SOFTMAX:
              SCREAMING_SNAKE_CASE_ :    List[str]       		=						softmax(SCREAMING_SNAKE_CASE_)
          elif function_to_apply == ClassificationFunction.NONE:
              SCREAMING_SNAKE_CASE_ :    Dict       		=						outputs
          else:
              raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}')

          if top_k == 1 and _legacy:
              return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

          SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						[
              {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(SCREAMING_SNAKE_CASE_)
          ]
          if not _legacy:
              dict_scores.sort(key=lambda lowercase_: x["score"]		,      reverse=SCREAMING_SNAKE_CASE_)
              if top_k is not None:
                  SCREAMING_SNAKE_CASE_ :    Dict       		=						dict_scores[:top_k]
          return dict_scores




"""simple docstring"""






class 			lowercase					:






			def __init__(self  :	Dict			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  {}






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	None:





				"""simple docstring"""


				print(self.vertex			)
				for i in self.vertex:
					print(SCREAMING_SNAKE_CASE_      ,''' -> '''      ,''' -> '''.join([str(SCREAMING_SNAKE_CASE_			) for j in self.vertex[i]]			)			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	int			)       ->	None:





				"""simple docstring"""


				if from_vertex in self.vertex:
					self.vertex[from_vertex].append(SCREAMING_SNAKE_CASE_			)
				else:
					# else make a new vertex
					lowerCAmelCase												=  [to_vertex]






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  [False] * len(self.vertex			)

				# call the recursive helper function
				for i in range(len(self.vertex			)			):
					if not visited[i]:
						self.dfs_recursive(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	list			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  True

				print(SCREAMING_SNAKE_CASE_      ,end=''' '''			)

				# Recur for all the vertices that are adjacent to this node
				for i in self.vertex:
					if not visited[i]:
						self.dfs_recursive(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
					UpperCAmelCase        =				Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					print("""DFS:""")
					g.dfs()

					# OUTPUT:
					# 0  ->  1 -> 2
					# 1  ->  2
					# 2  ->  0 -> 3
					# 3  ->  3
					# DFS:
					#  0 1 2 3


